The ability to think for themselves
Knowledge, critical judgment, creativity and the confidence to ask good questions—even when answers come easily.
Evidence for better education decisions
Education in the AI era.
Connect what research tells us with what districts are doing.
PUBLIC RESEARCH REGISTRYWorking evidence · evolving practice
43indexed library entries158districts in the source roster9Bay Area counties25 / 158districts reviewed or partial
K–12 AI Compass · For the Bay Area’s next generation
A different world.
A thoughtful start.
Preparing Bay Area children for a world we’re still learning to understand.
Our children will inherit a world different from the one we grew up in. We want them to have the knowledge, judgment, curiosity and human connections to help shape it.
K–12 AI Compass brings together research, district practices and community experience to help families and educators make thoughtful choices about AI—and learn what helps children thrive.
Our guiding question
What do children need to thrive?
We begin with children’s development and the lives we hope they can lead. The tools we choose should serve those aims.
Agency and connection
Room to make choices, take responsibility, learn through effort and build meaningful relationships with others.
Opportunity for every child
Learning that respects different needs and circumstances, with attention to whose opportunities expand and who may be left behind.
Find your starting point
Turn questions into informed choices.
Understand the evidence ↗
What does the research tell us?
Search findings, methods and limitations. Explore what we know and the questions still open.
Explore your district ↗How are local schools responding?
Compare documented plans and practices across the nine-county Bay Area, with sources and gaps visible.
Try a thoughtful next step ↗What could we learn by trying?
Use a small pilot guide, adapt a template and decide what evidence would justify continuing.
Learn with the community ↗What are you seeing?
Bring a question or share an experience. Community contributions publish immediately, are subject to moderation, and stay distinct from reviewed research.
How we work
Open evidence.
Honest uncertainty.
We distinguish research findings from interpretation, plans from implementation, and activity from measured impact. We welcome contrary findings, unsuccessful trials and corrections.
This is a working, AI-assisted collection. The district comparisons describe public documentation; they are not rankings of school quality. Our mission is to support better questions and better decisions as the evidence develops.
Where we stand · September 8, 2026
A growing public record.
43research and policy records
158districts in the nine-county roster
25district profiles with some AI-specific evidence
7 profiles have complete documentation scores; 18 are partial. Three additional searches remain unresolved, and 130 districts await review. Source checks vary in depth; coverage does not mean a comprehensive audit.
Evidence library
Search the underlying record.
Explore academic research, policy documents and implementation evidence. Each entry preserves the source finding, evidence characterization and its limitations.
43 unique source URLs
| Source | Type | Domain | Evidence | Finding & limitation |
|---|---|---|---|---|
| What should teachers teach and students learn in a future of powerful AI? OECD · 2025 Summary checked · September 8, 2026 · Publisher abstract | Policy paper | Curriculum | Descriptive / conceptual; not causal | Asks whether AI changes curricular priorities, which human competencies remain important even when AI can perform related tasks, and whether new competencies emerge.Limitations / open questionConceptual and anticipatory; does not establish causal effects or district-level cost. |
| Evolving AI capabilities and the school curriculum OECD · 2025 Summary checked · September 8, 2026 · Publisher abstract | Working paper | Curriculum | Descriptive / conceptual; not causal | Proposes a systematic framework for relating evolving AI capabilities to curricular aims, illustrated through writing.Limitations / open questionRequires empirical testing across subjects, ages and systems. |
| Artificial Intelligence and the Future of Skills OECD · ongoing Summary checked · September 8, 2026 · Project objectives and capability indicators | Framework | Curriculum | Descriptive / conceptual; not causal | Develops methods for comparing AI/robotics capability with human skills to inform policy and education planning.Limitations / open questionCapability measures do not specify educational interventions. |
| OECD Teaching Compass OECD · 2025 Summary checked · September 8, 2026 · Teaching Compass: teacher agency, well-being and competencies | Policy paper | Teachers | Descriptive / conceptual; not causal | A conceptual compass organized around teacher agency, well-being and competencies, positioning teachers as participants in curriculum change.Limitations / open questionDirection rather than intervention-level causal evidence. |
| AI Competency Framework for Students UNESCO · 2024 Summary checked · September 8, 2026 · Framework overview: 12 competencies / four dimensions | Framework | AI literacy | Descriptive / conceptual; not causal | Defines 12 competencies across human-centred mindset, ethics, AI techniques/applications and AI system design.Limitations / open questionPrimarily addresses what students should know about AI, not the broader redesign of human capability development because AI exists. |
| AI Competency Framework for Teachers UNESCO · 2024 Summary checked · September 8, 2026 · Framework overview | Framework | Teachers | Descriptive / conceptual; not causal | Defines knowledge, skills and values teachers need for responsible AI use while protecting human agency.Limitations / open questionDoes not quantify implementation burden or outcome effects. |
| Artificial Intelligence and the Futures of Learning UNESCO · ongoing Summary checked · September 8, 2026 · Strands of work | Framework | Policy | Descriptive / conceptual; not causal | Umbrella program spanning policy dialogue, generative-AI guidance and competency frameworks for students and teachers.Limitations / open questionBroad global guidance; implementation evidence varies by context. |
| Leading Through Uncertainty: State Approaches to AI in K–12 Education CRPE · 2026 Summary checked · September 8, 2026 · Key Findings and study scope | Landscape study | Policy | Descriptive / conceptual; not causal | Across 39 states/territories and 18 partner organizations, finds more vision/guidance than operational support for evaluation, procurement, evidence-building and scaling.Limitations / open questionDescribes activity rather than measuring student outcomes. |
| 2026 AI State Early Adopter Database CRPE · 2026 Summary checked · September 8, 2026 · Database description and selection criteria | Landscape study | Policy | Descriptive / conceptual; not causal | Tracks policy, governance, partnerships and implementation across 20 early-adopter states.Limitations / open questionTracks actions, not comparative effectiveness. |
| States and AI: An Early Look at Early Adopters CRPE · 2026 Summary checked · September 8, 2026 · Key findings | Landscape study | Policy | Descriptive / conceptual; not causal | Documents cautious experimentation across state systems amid uncertainty and capacity constraints.Limitations / open questionNon-evaluative scan; does not rank policies by efficacy. |
| Metacognition and self-regulation Education Endowment Foundation · updated 2025 Summary checked · September 8, 2026 · Key findings and evidence security | Evidence synthesis | Human capabilities | Synthesis; heterogeneous evidence | EEF reports high average impact (+8 months) at very low cost, emphasizing planning, monitoring, evaluating and explicit teaching of metacognitive strategies.Limitations / open questionNot AI-specific; translation into AI-rich learning environments needs direct testing.Additional note from original entry 31Large evidence base reports high average impact at low monetary cost, centered on planning, monitoring and evaluating one's own learning.Why it matters / limitationNot AI-specific, but potentially foundational to preserving agency and preventing cognitive offloading. |
| Metacognition and self-regulation evidence review Education Endowment Foundation · 2025 Summary checked · September 8, 2026 · Evidence-review description (not underlying PDF) | Evidence synthesis | Human capabilities | Synthesis; heterogeneous evidence | Updated evidence review of classroom strategies and professional-development practices for metacognition and self-regulated learning.Limitations / open questionDoes not directly address AI-mediated learning or cognitive offloading. |
| Feedback Education Endowment Foundation Summary checked · September 8, 2026 · Key findings and effectiveness | Evidence synthesis | Human capabilities | Synthesis; heterogeneous evidence | Feedback approaches can improve learning, but effects vary and some approaches can be counterproductive. EEF emphasizes task-focused and self-regulation feedback and attention to teacher workload.Limitations / open questionModern generative-AI feedback may differ materially from historical digital interventions. |
| Individualised instruction Education Endowment Foundation · 2021 Summary checked · September 8, 2026 · Key findings and effectiveness | Evidence synthesis | Personalization | Synthesis; heterogeneous evidence | Individualised instruction shows moderate average benefits with substantial variation. EEF recommends supplementing classroom teaching while protecting high-quality teacher interaction.Limitations / open questionOlder, heterogeneous evidence base; generative-AI tutoring requires direct evaluation. |
| The Future of Jobs Report 2025 World Economic Forum · 2025 Summary checked · September 8, 2026 · Report digest: skills outlook | Employer survey | Future skills | Descriptive / conceptual; not causal | Employers expect major skill change by 2030; AI/big data rise rapidly while analytical thinking, resilience, creativity, leadership and lifelong learning remain core.Limitations / open questionEmployer expectations are not causal evidence for K–12 curriculum choices.Additional note from original entry 34Employers expect rapid growth in AI/data skills while analytical thinking, resilience, creativity, leadership and lifelong learning remain core.Why it matters / limitationEmployer forecasts are informative but should not determine K–12 aims by themselves. |
| One Click Away: AI Tutoring with Khanmigo in a Two-Year School Experiment NBER · Oreopoulos & Low · 2026 Summary checked · September 8, 2026 · Working-paper abstract | Cluster RCT | Personalization | Causal study; context-limited | Two-year experiment in 18 Tennessee middle schools. Khanmigo assignment produced modest math gains; active use was limited and gains resembled Khan Academy practice without AI.Why it matters / limitationStrong counterweight to hype: availability does not guarantee substantive tutoring use, and AI may not outperform well-designed non-AI practice in routine deployment. |
| AI tutoring outperforms in-class active learning: an RCT Scientific Reports · Kestin et al. · 2025 Summary checked · September 8, 2026 · Article abstract and study context | Randomized controlled trial | Personalization | Causal study; context-limited | In a Harvard physics course, a carefully engineered AI tutor produced larger learning gains in less time than an active-learning classroom condition.Why it matters / limitationShows potential under high-quality pedagogical design, but in higher education, one subject, one course, and a custom tutor—not generic chatbot use. |
| A systematic review of AI-driven intelligent tutoring systems in K–12 education npj Science of Learning · 2025 Summary checked · September 8, 2026 · Review abstract and scope | Systematic review | Personalization | Synthesis; heterogeneous evidence | Reviews K–12 intelligent tutoring evidence and finds encouraging results alongside substantial heterogeneity in design, context and implementation.Why it matters / limitationUseful benchmark for what AI tutoring has actually demonstrated versus what remains uncertain. |
| Generative Artificial Intelligence in Teaching and Learning at the K–12 Level: A Systematic Review Technology, Knowledge and Learning · Marzano · 2025 Summary checked · September 8, 2026 · Review abstract and conclusion | Systematic review | Policy | Synthesis; heterogeneous evidence | Reviews 197 studies and identifies opportunities in personalization, assessment and engagement, while emphasizing teacher training, ethics, privacy and the lack of concrete classroom experiments.Why it matters / limitationMaps a rapidly expanding literature but much of the underlying evidence remains exploratory rather than causal. |
| A systematic literature review of generative AI literacy in schools Computers & Education: Artificial Intelligence · 2025 Summary checked · September 8, 2026 · Publisher abstract | Systematic review | AI literacy | Synthesis; heterogeneous evidence | Synthesizes 51 empirical studies and proposes five GenAI literacy competencies: understanding, use, evaluation, ethics and attitudes.Why it matters / limitationStudents often struggle with critical evaluation of AI outputs; the field remains young and policy-sensitive. |
| A systematic review on how educators teach AI in K–12 education Educational Research Review · Liu & Zhong · 2024 Summary checked · September 8, 2026 · Publisher-indexed summary; 45 empirical studies | Systematic review | AI literacy | Synthesis; heterogeneous evidence | Reviews how AI is taught in K–12 and finds emphasis on experiential learning, middle-school contexts and AI literacy development.Why it matters / limitationAddresses teaching about AI more than redesigning learning because AI exists. |
| Reimagining teacher development in the era of generative AI: A scoping review Teaching and Teacher Education · 2025 Metadata confirmed; summary unresolved · September 8, 2026 | Scoping review | Teachers | Not yet assessed | Do not rely on this summary yet. Publisher access remains unavailable. Title and DOI were confirmed in Crossref; no publisher abstract was available for verification. Unverified inherited summaryReviews 22 empirical studies of GenAI in teacher development, highlighting resource creation and tailored support while noting small samples and underrepresentation of in-service teachers.Why it matters / limitationTeacher development is central to implementation, yet the evidence base is still immature. |
| A New Direction for Students in an AI World: Prosper, Prepare, Protect Brookings Institution · 2026 Summary checked · September 8, 2026 · Report overview and Prosper / Prepare / Protect | Global task-force synthesis | Policy | Synthesis; heterogeneous evidence | Drawing on over 400 studies and international consultation, the task force judges that current risks of generative AI in children’s education overshadow its benefits. It recommends Prosper, Prepare and Protect actions while recognizing that carefully designed uses may support learning.Limitations / interpretationA task-force synthesis and recommendations, not a causal estimate of net benefit or harm for every tool or child. |
| How Teens Use and View AI Pew Research Center · 2026 Summary checked · September 8, 2026 · Survey overview and schoolwork section | National survey | Policy | Descriptive / conceptual; not causal | National survey of teens and parents: 54% of teens report chatbot use for schoolwork, and 10% say chatbots help with all or most schoolwork.Why it matters / limitationCritical baseline on real behavior and equity differences; self-reported use does not establish learning effects. |
| AI Index 2025 — Education Chapter Stanford HAI · 2025 Summary checked · September 8, 2026 · Education chapter: CS teacher readiness | Annual evidence compendium | Teachers | Descriptive / conceptual; not causal | The education chapter documents gaps in access to computer science and readiness to teach AI. Its teacher-readiness finding concerns computer-science teachers, not all teachers.Limitations / interpretationLandscape indicators drawn from specific populations; do not generalize CS-teacher readiness to the full workforce. |
| Empowering Education Leaders: A Toolkit for Safe, Ethical, and Equitable AI Integration U.S. Department of Education · 2024 Summary checked · September 8, 2026 · Toolkit title, scope and non-regulatory guidance | Federal toolkit | Policy | Descriptive / conceptual; not causal | Provides a decision framework for leaders incorporating AI into student learning and the instructional core, with emphasis on safety, ethics and equity.Why it matters / limitationImportant governance baseline; not a transformation or cost-effectiveness model. |
| Guidance for the Safe and Effective Use of AI in California Public Schools California Department of Education · 2025/2026 Summary checked · September 8, 2026 · Introduction and guidance sections | State guidance | Policy | Descriptive / conceptual; not causal | California TK–12 guidance covering human-centered AI, AI literacy, equitable access, academic integrity, data privacy and instructional integration.Why it matters / limitationDirectly relevant to SRVUSD and California implementation; guidance is intentionally non-mandatory and does not establish efficacy. Additional note from original entry 48California guidance for TK–12 covers human-centered AI, AI literacy, equitable access, academic integrity, data privacy, educator support and instructional practice.Why it matters / limitationGuidance establishes constraints and recommended practice; it does not determine which instructional interventions are most effective. |
| Model Policy: Artificial Intelligence in Education California Department of Education · 2026 Summary checked · September 8, 2026 · Introduction and model-policy scope | State model policy | Policy | Descriptive / conceptual; not causal | Model policy developed under SB 1288 covering legal, ethical and logistical considerations for California LEAs.Why it matters / limitationUseful implementation baseline and constraint set for any district pilot portfolio. |
| Learning Systems: The Landscape of Artificial Intelligence in K–12 Education Bellwether · 2024, updated August 2026 Summary checked · September 8, 2026 · Publisher series overview | Landscape report | Policy | Descriptive / conceptual; not causal | Bellwether’s Learning Systems series describes AI capabilities, policy considerations and possible education use cases. Its Foundations and Considerations reports were updated in August 2026.Why it matters / limitationPolicy-oriented synthesis; evidence quality varies by use case. |
| How Has Artificial Intelligence Been Used in Education? Institute of Education Sciences / REL Northwest · 2024 Summary checked · September 8, 2026 · Resource summary and five use categories | Research review | Policy | Synthesis; heterogeneous evidence | Organizes AI use into instruction/tutoring, personalized learning, assessment, analytics and administrative tasks, with four implementation considerations.Why it matters / limitationUseful taxonomy; predates much of the newest generative-AI evidence. |
| K-12 AI Curricula: A Mapping of Government-Endorsed AI Curricula UNESCO · 2022 (updated 2026) Summary checked · September 8, 2026 · Global survey overview | Global curriculum mapping | AI literacy | Descriptive / conceptual; not causal | Maps government-endorsed K–12 AI curricula globally, including content, learning outcomes, curriculum integration, teacher training, tools and pedagogy. Finds project-based learning common and recommends product-agnostic AI education.Why it matters / limitationDescribes curricula and implementation structures rather than causal learning outcomes. |
| Preparing K–12 Students With AI Literacy: Proposed Framework, Progression, and Task Design Principles ETS · 2025 Summary checked · September 8, 2026 · Publisher abstract | Research framework | AI literacy | Descriptive / conceptual; not causal | Proposes an AI-literacy conceptual framework, hypothesized learning progression and explicit assessment-design principles for K–12 learners.Why it matters / limitationFramework and task-design guidance; learning progression still requires broad empirical validation. |
| What Is AI Literacy? Competencies and Design Considerations ACM CHI · Long & Magerko · 2020 Summary checked · September 8, 2026 · Author manuscript abstract / framework | Foundational research paper | AI literacy | Descriptive / conceptual; not causal | Synthesizes interdisciplinary literature into an AI-literacy framework and design considerations for understanding, interacting with and evaluating AI. It is conceptual guidance, not a classroom intervention test.Limitations / interpretationPredates modern generative AI. Broad curriculum and assessment validity requires separate evidence. |
| AI4K12 — Five Big Ideas and K–12 AI Guidelines AAAI / CSTA / AI4K12 · 2019–ongoing Summary checked · September 8, 2026 · Five Big Ideas and draft guidelines | Curriculum guidelines | AI literacy | Descriptive / conceptual; not causal | Organizes K–12 AI learning around perception, representation/reasoning, learning, natural interaction and societal impact, with grade-band progression guidance.Why it matters / limitationPrimarily a content/curriculum framework for learning about AI, not evidence of broader educational transformation. |
| AI Literacy: A Framework to Understand, Evaluate, and Use Emerging Technology Digital Promise · 2024 Summary checked · September 8, 2026 · Framework: Understand, Evaluate, Use | Practice framework | AI literacy | Descriptive / conceptual; not causal | Frames AI literacy around understanding, evaluating and using AI, integrating technical foundations with ethics, human judgment, power, bias and agency.Why it matters / limitationResearch-informed but not itself a causal evaluation of a K–12 implementation. |
| AI Literacy Insights and Opportunities Digital Promise · 2026 Summary checked · September 8, 2026 · Landscape scan and recommendations | Landscape synthesis | AI literacy | Synthesis; heterogeneous evidence | Argues AI literacy should connect socio-technical competencies to computational foundations and explicitly support complementary human capabilities such as judgment, uncertainty navigation and collaboration.Why it matters / limitationStrategic synthesis rather than experimental outcome evidence. |
| Artificial Intelligence in K-12 Education: A Systematic Review of Teachers’ Professional Development Needs for AI Integration Computers · Aravantinos et al. · 2026 Summary checked · September 8, 2026 · Publisher reprint: PDF page 336, abstract | Systematic review | Teachers | Synthesis; heterogeneous evidence | Finds technical training alone insufficient; effective integration requires pedagogical knowledge, positive attitudes, organizational support, AI literacy, ethics and continuous professional development.Why it matters / limitationUnderlying studies are heterogeneous and concentrated geographically; the review does not quantify district-level implementation cost. |
| Unpacking Artificial Intelligence in Elementary Education: A Comprehensive Thematic Analysis Systematic Review Computers & Education: Artificial Intelligence · 2025 Summary checked · September 8, 2026 · Publisher-indexed scope and themes | Systematic review | Child development | Synthesis; heterogeneous evidence | Reviews AI applications in elementary education during 2013–2023, organizing themes and identifying gaps. Its scope predates much of the current generative-AI classroom experience.Limitations / interpretationA thematic review does not establish causal effects, long-term child-development outcomes or the effectiveness of a particular product. |
| Examining Student Teachers’ Agency in an AI-Supported Learning Environment International Journal of Artificial Intelligence in Education · 2025 Summary checked · September 8, 2026 · Article abstract (22 Q sorts) | Empirical qualitative study | Agency | Descriptive / conceptual; not causal | Identifies distinct patterns of AI-supported agency, including balanced intentional use and uncritical reliance associated with diminished criticality and agency.Why it matters / limitationSmall Q-methodology sample of student teachers in one higher-education context; not K–12 causal evidence. |
| Developing and Validating the Student Learning Agency Scale in Generative AI-Supported Contexts Education and Information Technologies · 2025 Summary checked · September 8, 2026 · Publisher abstract; university samples | Measurement study | Agency | Descriptive / conceptual; not causal | Develops a learning-agency scale for generative-AI-supported contexts using university-student samples. Instrument validation does not establish instructional effectiveness or applicability to K–12.Limitations / interpretationAbstract-level review only. The abstract contains inconsistent sample/factor counts, so numeric instrument details are not reproduced here. |
| Multidimensional Student Agency in Learning with AI: A Conceptual Framework and Design Implications AIED 2025 · Vincoli, Scholz & Nagashima · 2025 Summary checked · September 8, 2026 · Conference-paper abstract | Conceptual framework | Agency | Descriptive / conceptual; not causal | Defines student-AI agency across content, feedback/help, co-orchestration and data dimensions, offering a design lens for balancing system assistance with learner control.Why it matters / limitationConceptual framework; requires experimental validation across age groups and classroom contexts. |
| AI-based cognitive offloading by students in higher education in China Computers in Human Behavior: Artificial Humans · 2026 Summary checked · September 8, 2026 · Publisher abstract; university students in China | Measurement + observational study | Cognitive offloading | Descriptive / conceptual; not causal | Develops a scale distinguishing lower- and higher-order AI offloading and studies associations with AI value, usage and evaluation capabilities among university students in China. The reported relationships are observational.Limitations / interpretationNot K–12 evidence; associations do not show that AI causes loss of learning or that evaluation training prevents it. |
| Students Are Using Large Language Models and AI Detectors Can Often Detect Their Use Frontiers in Education · 2024 Summary checked · September 8, 2026 · Article abstract and detector limitations | Empirical study | Assessment | Descriptive / conceptual; not causal | Finds substantial student LLM use and ambiguity around ethical use; tested detectors produced enough error that authors warn they should not be relied upon alone for academic-integrity decisions.Why it matters / limitationSingle university course and detector generation from 2024; detector performance changes quickly and is not K–12-specific. |
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43 source records, with duplicate entries consolidated. Evidence labels are editorial aids, not formal GRADE ratings. See Methods for scope and provenance.
Community research contributions
Open submissions from readers, published immediately and subject to moderation. These have not been editorially reviewed and are separate from the evidence collection above.
Contribute research ↗Comparative matrix
New review batch: 17 evidence profiles added, 3 searches unresolved. See county coverage and review limits ↗
Documentation scores, not performance ratings.
Click a score to see its source and rationale. 25 profiles have some AI-specific evidence; 3 searches are unresolved and 130 districts remain unaudited. A plan is not proof of implementation.
Default order: highest total documentation score. Total = sum of 15 equally weighted dimensions, maximum 30. Only fully scored rows receive a total; unknowns stay last in either direction. Click any column heading to sort; click again to reverse. Ties use district name. Totals sum reviewed documentation judgments and are not measures of effectiveness.
Compare district documentation.
158 districts reconciled with the CDE’s 2026–27 list. Compare 15 dimensions of documented public activity. Seven districts have complete documentation scores, 18 are partial, 3 searches are unresolved and 130 await review; this is not a district-quality ranking.
2 Clearly documented1 Emerging / partial0 Not found in reviewed sources— Unknown / not yet audited
158 districts
Scroll horizontally for all 15 dimensions and source links →| Alameda Unified Alameda Research pending | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | Not yet audited. No inference should be drawn from blank cells. |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Albany City Unified Alameda Research pending | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | Not yet audited. No inference should be drawn from blank cells. |
| Berkeley Unified Alameda Research pending | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | Not yet audited. No inference should be drawn from blank cells. |
| Castro Valley Unified Alameda Research pending | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | Not yet audited. No inference should be drawn from blank cells. |
| Dublin Unified Alameda Partial | — | Indexed primary-document excerpt; direct PDF fetch returned 403. Resolution calls for unified guidance; adoption and present implementation remain unverified. Review: September 8, 2026. | |||||||||||||||
| Emery Unified Alameda Research pending | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | Not yet audited. No inference should be drawn from blank cells. |
| Fremont Unified Alameda Research pending | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | Not yet audited. No inference should be drawn from blank cells. |
| Hayward Unified Alameda Research pending | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | Not yet audited. No inference should be drawn from blank cells. |
| Livermore Valley Joint Unified Alameda Partial | — | Single-school program page; 2025–26 offering. After-school STEAM listing includes AI; no inference about district-wide curriculum. Review: September 8, 2026. | |||||||||||||||
| Mountain House Elementary Alameda Research pending | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | Not yet audited. No inference should be drawn from blank cells. |
| New Haven Unified Alameda Research pending | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | Not yet audited. No inference should be drawn from blank cells. |
| Newark Unified Alameda Research pending | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | Not yet audited. No inference should be drawn from blank cells. |
| Oakland Unified Alameda Partial | — | Resource-index review; embedded documents unresolved; no district aggregate. Reviewed September 8, 2026. Open the profile for each cell’s source, locator and rationale. | |||||||||||||||
| Piedmont City Unified Alameda Research pending | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | Not yet audited. No inference should be drawn from blank cells. |
| Pleasanton Unified Alameda Partial | — | First-person announcement only; handbook not reviewed. Co-lead reports a guidance release and staff-learning plans; underlying policy details need verification. Review: September 8, 2026. | |||||||||||||||
| San Leandro Unified Alameda Research pending | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | Not yet audited. No inference should be drawn from blank cells. |
| San Lorenzo Unified Alameda Research pending | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | Not yet audited. No inference should be drawn from blank cells. |
| Sunol Glen Unified Alameda Research pending | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | Not yet audited. No inference should be drawn from blank cells. |
| Acalanes Union High Contra Costa Partial | — | District AI hub review; linked guidance and board records not fully reviewed. Hub describes student access and accompanying lessons; no learning-effect claim. Review: September 8, 2026. | |||||||||||||||
| Antioch Unified Contra Costa Research pending | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | Not yet audited. No inference should be drawn from blank cells. |
| Brentwood Union Elementary Contra Costa Research pending | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | Not yet audited. No inference should be drawn from blank cells. |
| Byron Union Elementary Contra Costa Research pending | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | Not yet audited. No inference should be drawn from blank cells. |
| Canyon Elementary Contra Costa Research pending | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | Not yet audited. No inference should be drawn from blank cells. |
| John Swett Unified Contra Costa Research pending | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | Not yet audited. No inference should be drawn from blank cells. |
| Knightsen Elementary Contra Costa Research pending | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | Not yet audited. No inference should be drawn from blank cells. |
| Lafayette Elementary Contra Costa Research pending | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | Not yet audited. No inference should be drawn from blank cells. |
| Liberty Union High Contra Costa Research pending | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | Not yet audited. No inference should be drawn from blank cells. |
| Martinez Unified Contra Costa Research pending | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | Not yet audited. No inference should be drawn from blank cells. |
| Moraga Elementary Contra Costa Research pending | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | Not yet audited. No inference should be drawn from blank cells. |
| Mt. Diablo Unified Contra Costa Analyzed | 16 | District family FAQ review; linked documents excluded. Detailed family guidance covers classroom permissions, AI literacy and tool review. No outcome evaluation appears in this FAQ. Review: September 8, 2026. | |||||||||||||||
| Oakley Union Elementary Contra Costa Research pending | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | Not yet audited. No inference should be drawn from blank cells. |
| Orinda Union Elementary Contra Costa Research pending | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | Not yet audited. No inference should be drawn from blank cells. |
| Pittsburg Unified Contra Costa Research pending | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | Not yet audited. No inference should be drawn from blank cells. |
| San Ramon Valley Unified Contra Costa Analyzed | 21 | District public-page review. Reviewed September 8, 2026. Open the profile for each cell’s source, locator and rationale. | |||||||||||||||
| Walnut Creek Elementary Contra Costa Research pending | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | Not yet audited. No inference should be drawn from blank cells. |
| West Contra Costa Unified Contra Costa Research pending | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | Not yet audited. No inference should be drawn from blank cells. |
| Bolinas-Stinson Union Marin Research pending | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | Not yet audited. No inference should be drawn from blank cells. |
| Kentfield Elementary Marin Research pending | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | Not yet audited. No inference should be drawn from blank cells. |
| Laguna Joint Elementary Marin Research pending | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | Not yet audited. No inference should be drawn from blank cells. |
| Lagunitas Elementary Marin Research pending | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | Not yet audited. No inference should be drawn from blank cells. |
| Larkspur-Corte Madera Marin Research pending | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | Not yet audited. No inference should be drawn from blank cells. |
| Miller Creek Elementary Marin Research pending | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | Not yet audited. No inference should be drawn from blank cells. |
| Mill Valley Elementary Marin Research pending | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | Not yet audited. No inference should be drawn from blank cells. |
| Nicasio Marin Research pending | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | Not yet audited. No inference should be drawn from blank cells. |
| Novato Unified Marin Partial | — | District announcement republished on school site; agenda listing only. An AI education update was scheduled. The presentation, decisions and implementation remain unresolved. Review: September 8, 2026. | |||||||||||||||
| Reed Union Elementary Marin Research pending | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | Not yet audited. No inference should be drawn from blank cells. |
| Ross Elementary Marin Research pending | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | Not yet audited. No inference should be drawn from blank cells. |
| Ross Valley Elementary Marin Research pending | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | Not yet audited. No inference should be drawn from blank cells. |
| San Rafael City Elementary Marin Research pending | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | Not yet audited. No inference should be drawn from blank cells. |
| San Rafael City High Marin Research pending | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | Not yet audited. No inference should be drawn from blank cells. |
| Sausalito Marin City Marin Research pending | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | Not yet audited. No inference should be drawn from blank cells. |
| Shoreline Unified Marin Research pending | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | Not yet audited. No inference should be drawn from blank cells. |
| Tamalpais Union High Marin Partial | — | Historical single-course page; not current district policy. Course page discusses AI permissions, original analysis and teacher checkpoints. Review: September 8, 2026. | |||||||||||||||
| Calistoga Joint Unified Napa Research pending | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | Not yet audited. No inference should be drawn from blank cells. |
| Howell Mountain Elementary Napa Research pending | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | Not yet audited. No inference should be drawn from blank cells. |
| Napa Valley Unified Napa Partial | — | District progress bulletin; draft-stage account. Reports an AI Council meeting and feedback on draft guidance; later adoption is not verified here. Review: September 8, 2026. | |||||||||||||||
| Pope Valley Union Elementary Napa Research pending | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | Not yet audited. No inference should be drawn from blank cells. |
| Saint Helena Unified Napa Partial | — | Historical catalog excerpt; direct document access unresolved. An indexed course description mentions AI; current course delivery remains unknown. Review: September 8, 2026. | |||||||||||||||
| San Francisco Unified San Francisco Analyzed | 13 | Family and staff pages refreshed September 8, 2026; embedded documents excluded. Current student access remains uncertain because both pages warn of outdated content. | |||||||||||||||
| Bayshore Elementary San Mateo Research pending | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | Not yet audited. No inference should be drawn from blank cells. |
| Belmont-Redwood Shores Elementary San Mateo Research pending | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | Not yet audited. No inference should be drawn from blank cells. |
| Brisbane Elementary San Mateo Research pending | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | Not yet audited. No inference should be drawn from blank cells. |
| Burlingame Elementary San Mateo Research pending | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | Not yet audited. No inference should be drawn from blank cells. |
| Cabrillo Unified San Mateo Research pending | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | Not yet audited. No inference should be drawn from blank cells. |
| Hillsborough City Elementary San Mateo Research pending | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | Not yet audited. No inference should be drawn from blank cells. |
| Jefferson Elementary San Mateo Research pending | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | Not yet audited. No inference should be drawn from blank cells. |
| Jefferson Union High San Mateo Research pending | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | Not yet audited. No inference should be drawn from blank cells. |
| La Honda-Pescadero Unified San Mateo Research pending | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | Not yet audited. No inference should be drawn from blank cells. |
| Las Lomitas Elementary San Mateo Research pending | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | Not yet audited. No inference should be drawn from blank cells. |
| Menlo Park City Elementary San Mateo Evidence unresolved | — | AI-focused search plus general technology-plan page. General technology principles were located. AI-specific district evidence remains unresolved; Menlo School is a separate institution. Review: September 8, 2026. | |||||||||||||||
| Millbrae Elementary San Mateo Research pending | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | Not yet audited. No inference should be drawn from blank cells. |
| Pacifica San Mateo Research pending | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | Not yet audited. No inference should be drawn from blank cells. |
| Portola Valley Elementary San Mateo Research pending | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | Not yet audited. No inference should be drawn from blank cells. |
| Ravenswood City Elementary San Mateo Research pending | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | Not yet audited. No inference should be drawn from blank cells. |
| Redwood City Elementary San Mateo Research pending | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | Not yet audited. No inference should be drawn from blank cells. |
| San Bruno Park Elementary San Mateo Research pending | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | Not yet audited. No inference should be drawn from blank cells. |
| San Carlos Elementary San Mateo Research pending | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | Not yet audited. No inference should be drawn from blank cells. |
| San Mateo-Foster City San Mateo Partial | — | District working-group page and timeline; embedded slides excluded. Community representation is documented; policy drafting and implementation remain scheduled work. Review: September 8, 2026. | |||||||||||||||
| San Mateo Union High San Mateo Analyzed | 18 | District public-page review. Reviewed September 8, 2026. Open the profile for each cell’s source, locator and rationale. | |||||||||||||||
| Sequoia Union High San Mateo Partial | — | Five-page policy document review; historical revision. Policy identifies unauthorized AI-generated answers as cheating. Newer AI initiatives are outside this document review. Review: September 8, 2026. | |||||||||||||||
| South San Francisco Unified San Mateo Research pending | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | Not yet audited. No inference should be drawn from blank cells. |
| Woodside Elementary San Mateo Research pending | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | Not yet audited. No inference should be drawn from blank cells. |
| Alum Rock Union Elementary Santa Clara Research pending | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | Not yet audited. No inference should be drawn from blank cells. |
| Berryessa Union Elementary Santa Clara Research pending | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | Not yet audited. No inference should be drawn from blank cells. |
| Cambrian Santa Clara Research pending | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | Not yet audited. No inference should be drawn from blank cells. |
| Campbell Union Santa Clara Partial | — | District pilot narrative republished on school site; underlying presentation not reviewed. Reports math-tool pilots and a decision to expand Magma Math; no numerical effect sizes or comparison design provided. Review: September 8, 2026. | |||||||||||||||
| Campbell Union High Santa Clara Research pending | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | Not yet audited. No inference should be drawn from blank cells. |
| Cupertino Union Santa Clara Partial | — | Single-school handbook only; no district aggregate. Reviewed September 8, 2026. Open the profile for each cell’s source, locator and rationale. | |||||||||||||||
| East Side Union High Santa Clara Research pending | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | Not yet audited. No inference should be drawn from blank cells. |
| Evergreen Elementary Santa Clara Research pending | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | Not yet audited. No inference should be drawn from blank cells. |
| Franklin-McKinley Elementary Santa Clara Research pending | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | Not yet audited. No inference should be drawn from blank cells. |
| Fremont Union High Santa Clara Partial | — | LCAP targeted section review; historical budget plan. Instructional technology action includes AI-related teacher support. Its budget bundles several activities and is not AI spending. Review: September 8, 2026. | |||||||||||||||
| Gilroy Unified Santa Clara Research pending | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | Not yet audited. No inference should be drawn from blank cells. |
| Lakeside Joint Santa Clara Research pending | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | Not yet audited. No inference should be drawn from blank cells. |
| Loma Prieta Joint Union Elementary Santa Clara Research pending | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | Not yet audited. No inference should be drawn from blank cells. |
| Los Altos Elementary Santa Clara Research pending | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | Not yet audited. No inference should be drawn from blank cells. |
| Los Gatos Union Elementary Santa Clara Research pending | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | Not yet audited. No inference should be drawn from blank cells. |
| Los Gatos-Saratoga Union High Santa Clara Research pending | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | Not yet audited. No inference should be drawn from blank cells. |
| Luther Burbank Santa Clara Research pending | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | Not yet audited. No inference should be drawn from blank cells. |
| Milpitas Unified Santa Clara Partial | — | Historical superintendent report; current status unverified. Reports a Khanmigo pilot and plans for an advisory task force; no numerical outcomes provided. Review: September 8, 2026. | |||||||||||||||
| Moreland Santa Clara Research pending | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | Not yet audited. No inference should be drawn from blank cells. |
| Morgan Hill Unified Santa Clara Research pending | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | Not yet audited. No inference should be drawn from blank cells. |
| Mt. Pleasant Elementary Santa Clara Research pending | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | Not yet audited. No inference should be drawn from blank cells. |
| Mountain View Whisman Santa Clara Research pending | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | Not yet audited. No inference should be drawn from blank cells. |
| Mountain View-Los Altos Union High Santa Clara Analyzed | 14 | District public-page review. Reviewed September 8, 2026. Open the profile for each cell’s source, locator and rationale. | |||||||||||||||
| Oak Grove Elementary Santa Clara Research pending | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | Not yet audited. No inference should be drawn from blank cells. |
| Orchard Elementary Santa Clara Research pending | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | Not yet audited. No inference should be drawn from blank cells. |
| Palo Alto Unified Santa Clara Analyzed | 16 | District public-page review. Reviewed September 8, 2026. Open the profile for each cell’s source, locator and rationale. | |||||||||||||||
| San Jose Unified Santa Clara Analyzed | 17 | District public-page review. Reviewed September 8, 2026. Open the profile for each cell’s source, locator and rationale. | |||||||||||||||
| Santa Clara Unified Santa Clara Partial | — | Single-school policy page; not district-wide guidance. School policy addresses AI-generated work and independent assignments. Review: September 8, 2026. | |||||||||||||||
| Saratoga Union Elementary Santa Clara Research pending | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | Not yet audited. No inference should be drawn from blank cells. |
| Sunnyvale Santa Clara Research pending | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | Not yet audited. No inference should be drawn from blank cells. |
| Union Elementary Santa Clara Research pending | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | Not yet audited. No inference should be drawn from blank cells. |
| Benicia Unified Solano Evidence unresolved | — | AI-focused search plus general educational-services page. No AI-specific evidence was established in this bounded search. Board attachments and staff resources need further review. Review: September 8, 2026. | |||||||||||||||
| Dixon Unified Solano Research pending | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | Not yet audited. No inference should be drawn from blank cells. |
| Fairfield-Suisun Unified Solano Partial | — | Single-school event report on district website. Family event included an AI activity station; this does not establish district curriculum or policy. Review: September 8, 2026. | |||||||||||||||
| Travis Unified Solano Research pending | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | Not yet audited. No inference should be drawn from blank cells. |
| Vacaville Unified Solano Research pending | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | Not yet audited. No inference should be drawn from blank cells. |
| Vallejo City Unified Solano Research pending | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | Not yet audited. No inference should be drawn from blank cells. |
| Alexander Valley Union Elementary Sonoma Research pending | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | Not yet audited. No inference should be drawn from blank cells. |
| Bellevue Union Sonoma Research pending | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | Not yet audited. No inference should be drawn from blank cells. |
| Bennett Valley Union Elementary Sonoma Research pending | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | Not yet audited. No inference should be drawn from blank cells. |
| Cinnabar Elementary Sonoma Research pending | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | Not yet audited. No inference should be drawn from blank cells. |
| Cloverdale Unified Sonoma Research pending | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | Not yet audited. No inference should be drawn from blank cells. |
| Cotati-Rohnert Park Unified Sonoma Research pending | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | Not yet audited. No inference should be drawn from blank cells. |
| Dunham Elementary Sonoma Research pending | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | Not yet audited. No inference should be drawn from blank cells. |
| Forestville Union Elementary Sonoma Research pending | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | Not yet audited. No inference should be drawn from blank cells. |
| Fort Ross Elementary Sonoma Research pending | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | Not yet audited. No inference should be drawn from blank cells. |
| Geyserville Unified Sonoma Research pending | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | Not yet audited. No inference should be drawn from blank cells. |
| Gravenstein Union Elementary Sonoma Research pending | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | Not yet audited. No inference should be drawn from blank cells. |
| Guerneville Elementary Sonoma Research pending | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | Not yet audited. No inference should be drawn from blank cells. |
| Harmony Union Elementary Sonoma Research pending | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | Not yet audited. No inference should be drawn from blank cells. |
| Healdsburg Unified Sonoma Research pending | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | Not yet audited. No inference should be drawn from blank cells. |
| Horicon Elementary Sonoma Research pending | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | Not yet audited. No inference should be drawn from blank cells. |
| Kenwood Sonoma Research pending | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | Not yet audited. No inference should be drawn from blank cells. |
| Liberty Elementary Sonoma Research pending | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | Not yet audited. No inference should be drawn from blank cells. |
| Mark West Union Elementary Sonoma Research pending | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | Not yet audited. No inference should be drawn from blank cells. |
| Monte Rio Union Elementary Sonoma Research pending | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | Not yet audited. No inference should be drawn from blank cells. |
| Oak Grove Union Elementary Sonoma Research pending | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | Not yet audited. No inference should be drawn from blank cells. |
| Old Adobe Union Sonoma Research pending | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | Not yet audited. No inference should be drawn from blank cells. |
| Petaluma City Elementary Sonoma Research pending | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | Not yet audited. No inference should be drawn from blank cells. |
| Petaluma Joint Union High Sonoma Partial | — | Shared Petaluma City Schools hub; secondary provisions only. Secondary tool access and learning resources are published; this is not a separate audit of the elementary district. Review: September 8, 2026. | |||||||||||||||
| Piner-Olivet Union Elementary Sonoma Research pending | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | Not yet audited. No inference should be drawn from blank cells. |
| Rincon Valley Union Elementary Sonoma Research pending | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | Not yet audited. No inference should be drawn from blank cells. |
| Roseland Sonoma Research pending | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | Not yet audited. No inference should be drawn from blank cells. |
| Santa Rosa City Elementary Sonoma Research pending | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | Not yet audited. No inference should be drawn from blank cells. |
| Santa Rosa High Sonoma Research pending | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | Not yet audited. No inference should be drawn from blank cells. |
| Sebastopol Union Elementary Sonoma Research pending | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | Not yet audited. No inference should be drawn from blank cells. |
| Sonoma Valley Unified Sonoma Research pending | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | Not yet audited. No inference should be drawn from blank cells. |
| Twin Hills Union Elementary Sonoma Research pending | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | Not yet audited. No inference should be drawn from blank cells. |
| Waugh Elementary Sonoma Research pending | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | Not yet audited. No inference should be drawn from blank cells. |
| West Side Union Elementary Sonoma Research pending | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | Not yet audited. No inference should be drawn from blank cells. |
| West Sonoma County Union High Sonoma Research pending | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | Not yet audited. No inference should be drawn from blank cells. |
| Wilmar Union Elementary Sonoma Research pending | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | Not yet audited. No inference should be drawn from blank cells. |
| Windsor Unified Sonoma Evidence unresolved | — | AI-focused search plus general technology-services page. General technology goals and a plan link were located; AI-specific policy and activity remain unresolved. Review: September 8, 2026. | |||||||||||||||
| Wright Elementary Sonoma Research pending | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | Not yet audited. No inference should be drawn from blank cells. |
| Montgomery Elementary Sonoma Research pending | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | Not yet audited. No inference should be drawn from blank cells. |
| Two Rock Union Sonoma Research pending | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | Not yet audited. No inference should be drawn from blank cells. |
No matching districts
Try another county, status or search term.
Click a scored cell for its source, review scope and rationale. Unknown cells are not evidence of absence. Related research offers context, not proof that a district practice works. Read the district methodology ↗
Synthesis
What the evidence currently suggests.
Provisional interpretations of the reviewed source collection. Related records expose the underlying findings and limits; they do not establish every broader inference.
Increasingly clear
Curriculum questions are changing, not merely tool policies.Leading OECD work explicitly asks which human capabilities remain important even when AI can perform associated tasks.
Human capability and AI fluency are complements.Employer surveys highlight analytical thinking, creativity, adaptability and lifelong learning alongside rapidly growing technical skills.
Planning and reflecting on learning matter.Large pre-AI evidence bases support planning, monitoring, evaluating and self-regulated learning.
Not established
That more AI use produces more learning.Output quality, task completion and genuine learning are different outcomes.
Which district-wide approach offers the best value.The reviewed source collection does not establish district-wide AI cost-effectiveness, including teacher time and causal learning gains.
What schools should stop doing.Most guidance adds competencies and safeguards without specifying opportunity cost.
Expanded review · September 8, 2026
What the expanded evidence reveals.
20 additional districts investigated: 17 with bounded evidence profiles and 3 unresolved searches. Together with the original eight, evidence now spans all nine counties. This purposive sample does not estimate Bay Area prevalence.
Guidance and operating access are different kinds of evidence.
Acalanes publishes named student tools and describes accompanying lessons. Mt. Diablo publishes classroom and family expectations. These pages document access and guidance, not measured improvements in learning.
Acalanes evidence ↗ · Mt. Diablo evidence ↗Community involvement can precede policy.
San Mateo-Foster City lists student and parent working-group members while scheduling policy drafting and later implementation. Napa Valley reports feedback on draft guidelines. Participation should not be counted as completed policy or classroom delivery.
San Mateo-Foster City evidence ↗ · Napa Valley evidence ↗Pilot reports can inform questions without proving impact.
Campbell Union reports testing math tools and using student progress and teacher feedback to choose a next step. Its news account does not publish numerical effects or a comparison design. Milpitas describes a 2024 pilot whose current status still needs verification.
Campbell Union evidence ↗ · Milpitas evidence ↗A technology budget is not an AI cost estimate.
Fremont Union High’s 2024–25 plan bundles AI-related support with other instructional technology. The listed allocation cannot establish how much was spent on AI or whether it saved money.
Inspect the budget scope ↗Local examples need local labels.
Course pages, school policies and event reports add useful evidence, but do not establish district-wide practice. Three searches—Benicia, Menlo Park City and Windsor—remain unresolved. Unknowns are kept out of totals.
See review coverage and limitations ↗From public evidence to classroom trials
Practices you can put to work.
Three starting points for a small, deliberate trial. Each guide separates the documented district example from our suggested adaptation.
Use these as pilot guides.
The steps, templates and time estimates below are editorial suggestions, not district-reported procedures, costs or proven interventions. Source scope checked September 8, 2026.
Guide 01
Listen before expanding
For school leaders and student advisory groups
What the source documents
PAUSD publishes student listening-session and survey materials. This documents a feedback process, not evidence that listening caused better learning.
PAUSD · Student Perspectives & Feedback ↗ · District evidence trail
Try it on a small scale
- Invite students with varied experiences, including students who choose not to use AI. Make participation optional and avoid collecting identifiable student information.
- Use a short discussion to ask where AI helps, where it gets in the way, and what feels unclear or unfair.
- Group the themes, publish an anonymous summary, and name one change to test. Return to students to check whether it helped.
Planning estimate
One facilitator; allow 60–90 minutes of preparation and a 20–30 minute discussion. Adapt this estimate to your setting.
What to look for
Track which perspectives were represented, how many issues received a response, and whether students report clearer expectations at a follow-up. A small discussion cannot estimate district-wide opinion.
Student listening prompts — open to copy
Select and copy this text, then replace the brackets for your setting.
When has AI helped you understand something? When has it made learning harder? Which assignment rules are unclear? What should adults change first? Our next action: [change]. We will report back by [date].
Guide 02
Make assignment permissions explicit
For teachers and teaching teams
What the source documents
MVLA explains that teacher directions determine permitted AI use on assignments. Its guidance provides a policy example; it does not establish the learning effect of a particular permission model.
Try it on a small scale
- Choose one assignment and name the thinking students need to do themselves.
- State which kinds of assistance are allowed, which are restricted, and how students should acknowledge use. Follow your school’s existing policy and approved-tool rules.
- Ask students to explain the directions back to you. Review confusion and student work before adapting the next assignment.
Planning estimate
One teacher; allow 20–30 minutes to draft directions and 5 minutes to discuss them with students. Adapt this estimate to your setting.
What to look for
Track clarification questions, whether students can explain the rules, and the quality of independent explanations. Do not treat fewer reported incidents as proof of less misuse.
Assignment directions template — open to copy
Select and copy this text, then replace the brackets for your setting.
Learning goal: [what students should understand or do]. You may use AI for: [specific assistance, or none]. You must do yourself: [essential thinking and work]. Tools and access: [approved option and an equivalent route without AI]. Acknowledgement: [what to disclose and where]. Show your learning through: [explanation, draft history or demonstration].
Guide 03
Keep adoption connected to learning
For teaching teams and curriculum leaders
What the source documents
SRVUSD’s Technology Plan connects AI with student agency and authentic assessment. This is a published strategic direction; it is not proof of completed redesign or improved outcomes.
SRVUSD · Technology Plan, Academic Integrity ↗ · District evidence trail
Try it on a small scale
- Pick one learning objective and identify the evidence that would show a student understands it.
- Adapt one task to include a meaningful choice, visible reasoning and a brief independent explanation. Keep an accessible route that does not require an AI account.
- Use the same rubric to review work before and during the pilot. Discuss workload, access and student understanding before deciding to continue, revise or stop.
Planning estimate
A pair of teachers; allow one 45–60 minute planning meeting, plus normal assessment time and a 20-minute review. Adapt this estimate to your setting.
What to look for
Compare rubric evidence of independent understanding, student experience and teacher time. A classroom pilot can inform a local decision but cannot establish a causal effect.
Small pilot planning template — open to copy
Select and copy this text, then replace the brackets for your setting.
Learning objective: [objective]. Task and student choice: [description]. Where AI may help: [bounded role]. Independent evidence of learning: [demonstration]. Access and support: [equivalent options]. Review date: [date]. Continue if: [learning and workload criteria]. Revise or stop if: [criteria].
Teacher research ↗ · Find Palo Alto in the matrix ↗ · Discuss what you tried ↗
Research agenda
The questions worth closing.
These open questions guide what we investigate next. We welcome evidence that changes our conclusions.
Which human capabilities become more valuable as AI capabilities rise?
Which tasks must students still learn to do independently, even when AI can do them?
When does AI assistance deepen learning — and when does it create cognitive offloading?
What changes to assessment are required when high-quality generation is ubiquitous?
Which teacher-development models actually change classroom practice?
What is the cost per student and teacher-hour of each intervention?
What existing activities should be reduced or displaced to make room?
How do intervention effects differ by age, attainment, income, disability and access?
Which combination of changes could improve learning within a district’s resources?
Which findings replicate across districts — and which depend on local context?
From unanswered questions to district evidence
The initial district review exposes the same missing links: measured outcomes, teacher time, implementation costs and decisions to scale or stop. These gaps are priorities for further research, not proof of absence across the Bay Area.
See district patterns ↗ · Inspect the district matrix ↗Community-added research
What are we missing?
Researchers, educators, students, policymakers, technologists and community members are invited to add primary research, policy documents, datasets, district implementation reports and credible counter-evidence.
Contribution standard
We especially want evidence that challenges our interpretations, documents failed implementations, quantifies costs, or reports null results.
Submission does not imply endorsement.Research & district methodology
Make the limits visible.
Read source findings separately from interpretations, policy commitments separately from implementation, and documented activity separately from measured impact.
September 8, 2026 · Source review
43 unique source records received a review pass. 42 summaries were checked against the stated primary-source scope; one teacher-development review remains unresolved beyond its bibliographic metadata. Abstract and overview checks are explicitly identified and are not full-text critical appraisals.
Twenty-five profiles now contain at least some AI-specific evidence. Three further searches remain unresolved, and 130 districts have not received a review pass. Seven profiles have complete documentation totals; incomplete profiles have no aggregate. Scores are editorial judgments, not district-endorsed rankings.
The initial collection combined an evidence library and a district observatory. Duplicate source notes were consolidated. The district roster was reconciled with the CDE’s 2026–27 district list on September 8, 2026. AI assisted the research and synthesis; no independent second reviewer has signed off.
Research standards
01
Primary first
Prefer papers, datasets, statutes and district documents over commentary.
02
Fact ≠ inference
Record what the source finds separately from what we think it implies.
03
Contrary evidence
Actively index null findings, failed pilots and credible arguments that challenge our interpretations.
04
Version everything
Show date added, reviewer, classification changes and prior synthesis versions.
Source records show the scope and date of each check; district cells link to their rationale. A full reviewer history and prior synthesis versions are not yet available. Recent corrections appear below and on the About page.
District scope and scoring
The research universe is the nine-county Bay Area's public K–12 school districts. The roster matches the CDE’s 2026–27 list for the nine counties, excluding entries marked lapsed. The CDE itself cautions that directory information is locally self-reported and can contain omissions or errors, so roster corrections should be welcomed and versioned.
For this edition, the review corpus is the primary pages linked in each district profile. A 0 means a dimension was not found in those pages; it does not establish district-wide absence. Blank means unknown or not audited. Embedded documents not accessible during review are not treated as verified.
Scoring: 2 = explicit district-level guidance, strategy or published practice; 1 = planned, partial or school-level evidence; 0 = not found in the reviewed corpus; blank = unknown. For outcomes, 1 can indicate a reported monitoring process without published results; 2 requires documented results. For costs, 1 can indicate a relevant bundled allocation, while 2 requires AI-specific amounts. Neither is proof of effectiveness or actual spending unless explicitly stated. Totals require all 15 dimensions to be scored. All dimensions have equal weight; the total is a navigation aid, not a validated scale.
Roster sources: California Department of Education Public District Directory; Alameda, Contra Costa, Marin, Napa, San Mateo, Santa Clara, Solano and Sonoma county education-office district directories; SFUSD for San Francisco County.
Improve the record
Contribute a primary source, missing district document, correction, null result or cost study. Contributions remain separate from the reviewed corpus until editorial review.
Prepare a contribution ↗Corrections & review history
This edition consolidates duplicate records, adds access-check labels, distinguishes planned district actions from implementation, and introduces an owner-reviewed contribution inbox. SFUSD’s board-document index is not treated as proof of an OpenAI agreement. Cupertino’s school handbook is not treated as district-wide policy. Submit a correction with a primary source through the contribution form.
Historical source availability
On September 8, 2026, direct checks of the Saint Helena 2024–25 course catalog and Sequoia Union High academic integrity PDF returned “not found,” although both documents remain indexed by search engines. Their original citations are retained for provenance; these historical documents should not be assumed to describe current policy. Replacement copies remain to be verified.
Review coverage by county
25 profiles have some AI-specific evidence (7 scored across all dimensions, 18 partial); 3 searches remain unresolved; 130 have not been reviewed. A scored dimension measures documentation, not effectiveness. Initial reviews are bounded by the sources listed in each profile.
| County | Roster | Evidence profiles | Unresolved searches | Not reviewed |
|---|---|---|---|---|
| Alameda | 18 | 4 | 0 | 14 |
| Contra Costa | 18 | 3 | 0 | 15 |
| Marin | 17 | 2 | 0 | 15 |
| Napa | 5 | 2 | 0 | 3 |
| San Francisco | 1 | 1 | 0 | 0 |
| San Mateo | 23 | 3 | 1 | 19 |
| Santa Clara | 31 | 8 | 0 | 23 |
| Solano | 6 | 1 | 1 | 4 |
| Sonoma | 39 | 1 | 1 | 37 |
The second batch used targeted AI searches and public district or school pages, selected for geographic spread and source availability. It is not random, exhaustive or independently double-reviewed. Historical documents, indexed excerpts, first-person announcements and unreviewed attachments are labeled. A search that finds no AI-specific evidence does not establish absence.
San Francisco has one district in this roster. SFUSD was refreshed alongside 20 additions from the other eight counties. Petaluma’s shared administration is represented here by its high-school district only; its elementary district remains unaudited.
Roster correction · September 8, 2026
Reconciled all 158 entries with the CDE 2026–27 district list (Excel). Added Montgomery Elementary and Two Rock Union, which were missing. Removed Montebello Elementary (closed) and Kashia Elementary (listed as lapsed into Horicon) from the active roster. The total remains 158; Santa Clara now has 31 entries and Sonoma has 39. District names follow the state list; existing profile links are preserved.
CDE roster definitions and annual lists ↗ · Montebello directory status ↗
District evidence
Look beneath the matrix.
Choose a district for its source trail, or compare up to four. Plans, public guidance and measured outcomes are different kinds of evidence.
Side-by-side comparison
Select districts from the matrix or add the current profile. Reviewed scores describe documentation within the listed source corpus.
Evidence into decisions
Two decisions to make carefully.
Editorial recommendations grounded in the sources below. These are starting points for a local decision, not claims that one approach works everywhere.
Should we pilot AI tutoring?
What the evidence says. A Tennessee middle-school trial of Khan Academy plus Khanmigo reported modest math gains, with limited substantive chatbot use. It does not establish that adding AI outperforms comparable practice without it. A Harvard physics trial found benefits for a specifically designed tutor in a short undergraduate setting; transfer to K–12 remains uncertain.
Our recommendation. Run a bounded pilot with a comparable non-AI practice group, teacher supervision, and a clearly defined learning objective. Measure unaided retention, participation, teacher workload and full cost. Agree on expansion and stopping criteria before launch.
What would change this advice? Replicated K–12 evidence showing lasting learning benefits over a credible alternative, with reported implementation costs and subgroup outcomes.
Tennessee working paper ↗ · Harvard trial ↗ · K–12 tutoring review ↗What should teacher AI learning include?
What the evidence says. UNESCO provides a competency framework covering human-centred practice, ethics, AI foundations, pedagogy and professional learning. PAUSD publishes professional-learning materials and student feedback. These show a framework and documented activity, not proof of improved student outcomes.
Our recommendation. Start with one teaching problem per team. Give teachers time to test a workflow, check its accuracy and accessibility, and compare resulting student work. Include privacy, student agency and assessment design. Share failures alongside useful examples.
Measure. Track preparation time, correction time, quality of learning tasks and unaided student performance. Tool usage and workshop attendance are implementation measures, not sufficient evidence of learning.
UNESCO teacher framework ↗ · PAUSD documentation ↗Owner only
Research review inbox
New public research contributions appear immediately. Contact details, private submissions and review notes stay private. Archive a record to remove its public listing. Marking it ready does not add it to the reviewed evidence collection; that requires a separate editorial update.
Sign in with ChatGPTCommunity
What is your school trying?
Ask a question, share an experience, discuss evidence or suggest a correction. Tell us what you are learning—including what did not work.
Opening question from K–12 AI Compass
What learning improved—and how did you check?
Describe one AI-supported activity, the age group, the comparison you used and what students could do independently afterwards. What did not improve?
Opening question from K–12 AI Compass
Which AI assignment rules are clear to students?
Share a permission or acknowledgement approach students understand. What confusion remains? Please link public guidance where available.
Opening question from K–12 AI Compass
What would make a district AI pilot worth continuing?
Which learning outcomes, teacher workload measures, costs and stopping criteria would you agree on before a pilot starts?
Experience is valuable. Evidence needs review.
Posts and replies publish immediately and are subject to moderation. The owner may remove spam, abuse or personal information. Community contributions do not enter the reviewed research library automatically. Be respectful, disclose relevant interests, and include sources where possible. Do not identify students or post personal information about others. No ratings of teachers or districts.
Add your voice
K12 Future Fund · An initiative in formation
Investing in our
children’s future.
Help public schools prepare children for a world that is changing around them.
We are bringing together educators, families and founding supporters to build a fund for Bay Area public-school innovation—grounded in evidence, guided by teachers and focused on children.
Our goals
Put opportunity into children’s hands.
Support teacher-led innovation
Help public-school educators test thoughtful approaches to AI literacy, teaching and assessment, with time and resources to learn what works.
Expand access and agency
Prioritize children and schools with fewer resources. Build knowledge, independent judgment, creativity and the confidence to shape their futures.
Share what we learn
Support small, measurable pilots and publish their lessons, costs and outcomes—including results that challenge our expectations.
The funding model we aim to build
Our goal: 100% for public-school programs.
Our goal is for every dollar designated for school grants to reach public-school programs, with operating costs covered separately by founding supporters.
We envision an unpaid board and volunteer leadership, with no compensation taken from the school grant fund. Administration, insurance, payment processing and any fiscal-sponsor fees still need to be funded. Separate operating support must be secured before we can make an unconditional 100% commitment.
We intend to publish grants, recipients, program outcomes and a clear accounting of both grant funding and operating expenses. These are founding commitments we are working to establish, not a record of funds already raised or distributed.
Build this with us
Help shape the beginning.
Join the founding board effort
We invite expressions of interest from prospective unpaid board members with experience in public education, finance, nonprofit governance, fundraising or community leadership. Bring sound judgment, time to contribute and a commitment to children.
While the structure is being established, participants would serve as founding advisers. Formal board appointments will depend on the organization’s eventual structure; a fiscal sponsor would retain its own legal oversight.
Express board interest ↗Become a founding contributor
Help establish the first school grant fund, underwrite operating costs separately, or contribute professional expertise. We welcome conversations with individuals, families, businesses and foundations who share the mission.
We are gathering expressions of interest, not accepting payments or binding pledges through this site.
Discuss founding support ↗Bring a public-school perspective
Educators and school leaders: tell us which learning needs deserve attention, what a useful small grant could enable and how you would judge progress. Your experience can help shape the first program.
Share a school program idea ↗Help establish the structure
We welcome introductions to education-aligned fiscal sponsors and people with nonprofit legal, accounting or grant administration expertise.
Offer an introduction or expertise ↗Where we stand
Building the foundation first.
K12 Future Fund is a proposed initiative associated with K–12 AI Compass. It does not currently have recognized 501(c)(3) status, and no fiscal sponsorship arrangement has been established. We plan to explore sponsorship, recruit founding advisers and define an initial school grant program before launching fundraising.
No donations are being accepted on this page, and no claim of tax deductibility is made. We will publish the legal structure, oversight arrangements and funding terms when they are established.
Evidence comes first. Financial support will not purchase favorable research coverage, district scores or grant selection. Our proposed process will include conflict disclosures and documented selection criteria.
Start a conversation
What could you help make possible?
Tell us a little about yourself, your connection to public education and whether you are interested in board service, founding support, a school program or professional expertise.
Email opens in your email app. Please do not include student personal information.
About K–12 AI Compass
Better questions. Clearer evidence.
K–12 AI Compass brings education research and Bay Area district documentation together so educators, families and school leaders can make more informed decisions about AI.
Editorial responsibility
The site owner maintains the collection, reviews contributions and moderates the community. Research and summaries were prepared with AI assistance. This site is not a district publication, and listing a district, institution or product does not imply endorsement.
Publisher identity and funding or affiliation disclosures are awaiting confirmation. No claim of independence, nonprofit status or institutional sponsorship is made.
What our reviews mean
Each research record identifies the scope of the source check. Checking an abstract or publisher overview is not a full-text critical appraisal. District scores describe what is documented in the listed sources; they are not measures of district quality or proof of implementation. Community posts are experiences and opinions, separate from reviewed evidence.
Read the methods and limitations ↗Corrections and removal requests
Use the private correction form to identify a record or post and describe the problem. A supporting link is optional. For privacy or removal requests, avoid repeating sensitive information. Requests go only to the owner’s inbox; changes require review.
Submit a correction or removal request ↗Privacy and participation
Reading does not require submitting personal information. Community participation uses ChatGPT sign-in. The app stores a site-specific account identifier with posts and helpful reactions to support moderation and prevent duplicate reactions. Your chosen display name, role, district and contribution become visible immediately, subject to moderation; your account identifier is not included in public discussion responses.
Research contributions publish immediately in the community research section unless you choose the private option. Source details, explanation, limitations, display name and relationship are public. Earlier private submissions stay private. The owner inbox also stores optional contact details. A daily hash derived from connection information helps limit spam. Contact details and private review notes are not published in the library.
Posts, reactions and submissions are stored on the site’s hosted database. There is no automatic retention deadline; archived or rejected records may remain stored for moderation. You can request removal through the contribution form. Hosting and sign-in providers also process connection and account information under their own policies.
Do not submit identifiable student information or personal information about someone else. Use only a display name and optional background details you are comfortable sharing. No advertising or third-party analytics have been added to the site.
Recent corrections · September 8, 2026
- Re-scored 25 district profiles with evidence against named source sections, with unknowns preserved.
- Excluded unrelated SFUSD literacy results and an unverified agreement from AI scoring.
- Corrected the Brookings summary to reflect its stronger concern about risks to children.
- Corrected the cognitive-offloading study’s journal and higher-education scope.
- Replaced two inaccessible catalog links with publisher or full-text alternatives.